Managing data transformations and maintaining the quality of your pipelines can take up the largest part of a team’s time, keeping you from building new projects and exploring different initiatives. What happens when models based on your data continue to evolve, when similar transformations need to be applied on new datasets, production environments change, governance standards are enforced, or the number of projects to monitor increases exponentially?
Dataiku is best known as the platform for Everyday AI. Data is the source and, with Dataiku, you can build pipelines to create the information of today (analytics) and the insights of tomorrow (predictions). Dataiku enables teams to map production data quickly, test projects on production environments, set up pipeline metrics and data quality checks, and trigger pipelines to run on a schedule or based on conditions. This article will highlight how exactly Dataiku helps DataOps teams automate and manage their data pipelines.
1. Self-Contained, Deployable Projects
Dataiku projects are the central place for all work and collaboration, and where teams create and maintain related data products. Each Dataiku project has a visual flow that represents the pipeline of data transformations and movement from start to finish. Each data product can be structured and organized through data collections in order to allow other users to discover and reuse what has already been done.
DataOps automates the continuous feeding of data to live production models, repeating each preparation and transformation step used to build and train the models. It includes ensuring that timely, accurate data is available to populate analytics products (such as reports and dashboards), analytics applications, and production AI/ML models.
In Dataiku, users can see a timeline of recent activity and can leverage automatic flow documentation and project bundles so tracking changes and managing data pipeline versions in production is seamless.
See how Dataiku helps optimize, automate, and monitor the data lifecycle with consistency, transparency, and reproducibility in mind.
2. Data Quality Metrics and Checks
The lifecycle of a data or machine learning project doesn’t end once a flow is complete. To maintain workflows and improve models, teams must continuously feed them new data. Automation helps make this more efficient by reducing the amount of manual supervision. However, as we automate workflows, we are exposed to some risks, such as ingesting poor quality data without knowing it. This, in turn, could lead to broken workflows and, worse, obsolete models and dashboards.
In Dataiku, metrics automatically assess data or model elements for changes in quality or validity, and checks ensure that scheduled flows run within expected timeframes and that metrics deliver the expected results.
Plus, configurable alerts and warnings give teams the oversight they need to safely manage production pipelines, without the need to constantly (and manually) monitor.
3. Batch or Real-Time Deployments
Project bundles snapshot the data, logic, and dependencies needed to recreate and execute pipelines in QA or production environments. Teams can run scheduled jobs or expose elements as REST APIs to support real-time applications.
When it comes to project and model deployment in Dataiku, the deployer acts as a central place where operators can manage versions of Dataiku projects and API deployments across their individual lifecycles. It provides oversight over both types of deployments, and event logs and dashboards allow data operators to continuously monitor systems and detect issues.
4. Automation Scenarios and Triggers
Ready to automate repetitive sequential tasks like loading and processing data, running batch scoring jobs, retraining models, and updating documentation? Dataiku’s built-in scheduler, scenarios, enables this. Operators can use the visual interface or execute scenarios programmatically with APIs in order to configure partial or full pipeline execution based on time and condition-dependent triggers.
5. Smart Flow Operations
Now you can leave interrupted connections, broken dependencies, and out-of-sync schemas in the past with DataOps and orchestration in Dataiku. Flow-aware tooling helps operators manage pipeline dependencies, check for schema consistency, and intelligently rebuild datasets and sub-flows to reflect recent updates.
6. APIs and Git Integration
With robust APIs, teams can programmatically interact with and operate data projects from external systems and IDEs. With Git integration, teams can take advantage of project version control and traceability and easily incorporate external libraries, notebooks, and repositories for both code development and CI/CD purposes.
In conjunction with MLOps and ITOps capabilities, DataOps in Dataiku helps to ensure models and data pipelines can withstand stressors, remain robust, and keep running smoothly in production.